Abstract
This paper presents a completely unsupervised change detection approach for synthetic aperture radar (SAR) images based on stacked autoencoders (SAE). The proposed method innovatively implements the change detection task by establishing a differencing neural network with a novel cost function. Firstly, two SAR images are used to pre-train two stacked autoencoders, then these two stacked autoencoders are unrolled to initialize the parameters of differencing neural network. Next, a novel cost function, including the difference between bi-temporal features and an initial difference image, is designed to fine tune the networks for highlighting the changes. Finally, we can obtain the detection results by measuring the Euclidean distance between the outputs of the two neural networks. The experiments on real multitemporal SAR datasets prove the outstanding performance of the proposed method.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61602385).
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Chen, F., Shi, J., Gong, M. (2016). Differencing Neural Network for Change Detection in Synthetic Aperture Radar Images. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_38
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DOI: https://doi.org/10.1007/978-981-10-3611-8_38
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